2D cooperative inversion of direct current resistivity and gravity data: A case study of uranium bearing target rock
ABSTRACT Interpretation of a single geophysical data set is not sufficient to get complete subsurface information. Cooperative or joint inversion of geophysical data sets is the preferred method for most case studies. In the present study, we present the results of the cooperative inversion approach...
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| Vydáno v: | Geophysical Prospecting Ročník 67; číslo 3; s. 696 - 708 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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01.03.2019
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| ISSN: | 0016-8025, 1365-2478 |
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| Abstract | ABSTRACT
Interpretation of a single geophysical data set is not sufficient to get complete subsurface information. Cooperative or joint inversion of geophysical data sets is the preferred method for most case studies. In the present study, we present the results of the cooperative inversion approach of direct current resistivity and gravity data. The algorithm uses fuzzy c‐means clustering to determine the petrophysical relationship between density and resistivity to obtain the similarity. Synthetic data set has demonstrated that the cooperative inversion approach can produce more reliable and better resistivity and density models of the subsurface as compared to those obtained through individual inversions. To utilize the presented cooperative inversion algorithm, the number of geologic units (number of clusters) in the study region must be known a priori. As a field study, the cooperative inversion approach was used to identify the extension of uranium‐bearing target rock around the Beldih open cast mine. We noted the inconsistencies in both resistivity and density models obtained from the individual inversions. However, the presented cooperative inversion approach was able to produce similar resistivity and density models while maintaining the same error level of the respective individual inversions. We have considered four geologic units in the presented cooperative inversion as a field case study. We have also compared our cooperative results with drilled borehole and found to be a reliable tool to differentiate between the target rocks (kaolinite and quartz–magnetite–apatite rocks) and the ultramafic rock (host rock quartzite/alkaline granite). However, this study is subject to certain limitations such as the inability to differentiate between closely spaced kaolinite and quartz–magnetite–apatite rocks. |
|---|---|
| AbstractList | ABSTRACT
Interpretation of a single geophysical data set is not sufficient to get complete subsurface information. Cooperative or joint inversion of geophysical data sets is the preferred method for most case studies. In the present study, we present the results of the cooperative inversion approach of direct current resistivity and gravity data. The algorithm uses fuzzy c‐means clustering to determine the petrophysical relationship between density and resistivity to obtain the similarity. Synthetic data set has demonstrated that the cooperative inversion approach can produce more reliable and better resistivity and density models of the subsurface as compared to those obtained through individual inversions. To utilize the presented cooperative inversion algorithm, the number of geologic units (number of clusters) in the study region must be known a priori. As a field study, the cooperative inversion approach was used to identify the extension of uranium‐bearing target rock around the Beldih open cast mine. We noted the inconsistencies in both resistivity and density models obtained from the individual inversions. However, the presented cooperative inversion approach was able to produce similar resistivity and density models while maintaining the same error level of the respective individual inversions. We have considered four geologic units in the presented cooperative inversion as a field case study. We have also compared our cooperative results with drilled borehole and found to be a reliable tool to differentiate between the target rocks (kaolinite and quartz–magnetite–apatite rocks) and the ultramafic rock (host rock quartzite/alkaline granite). However, this study is subject to certain limitations such as the inability to differentiate between closely spaced kaolinite and quartz–magnetite–apatite rocks. Interpretation of a single geophysical data set is not sufficient to get complete subsurface information. Cooperative or joint inversion of geophysical data sets is the preferred method for most case studies. In the present study, we present the results of the cooperative inversion approach of direct current resistivity and gravity data. The algorithm uses fuzzy c‐means clustering to determine the petrophysical relationship between density and resistivity to obtain the similarity. Synthetic data set has demonstrated that the cooperative inversion approach can produce more reliable and better resistivity and density models of the subsurface as compared to those obtained through individual inversions. To utilize the presented cooperative inversion algorithm, the number of geologic units (number of clusters) in the study region must be known a priori . As a field study, the cooperative inversion approach was used to identify the extension of uranium‐bearing target rock around the Beldih open cast mine. We noted the inconsistencies in both resistivity and density models obtained from the individual inversions. However, the presented cooperative inversion approach was able to produce similar resistivity and density models while maintaining the same error level of the respective individual inversions. We have considered four geologic units in the presented cooperative inversion as a field case study. We have also compared our cooperative results with drilled borehole and found to be a reliable tool to differentiate between the target rocks (kaolinite and quartz–magnetite–apatite rocks) and the ultramafic rock (host rock quartzite/alkaline granite). However, this study is subject to certain limitations such as the inability to differentiate between closely spaced kaolinite and quartz–magnetite–apatite rocks. Interpretation of a single geophysical data set is not sufficient to get complete subsurface information. Cooperative or joint inversion of geophysical data sets is the preferred method for most case studies. In the present study, we present the results of the cooperative inversion approach of direct current resistivity and gravity data. The algorithm uses fuzzy c‐means clustering to determine the petrophysical relationship between density and resistivity to obtain the similarity. Synthetic data set has demonstrated that the cooperative inversion approach can produce more reliable and better resistivity and density models of the subsurface as compared to those obtained through individual inversions. To utilize the presented cooperative inversion algorithm, the number of geologic units (number of clusters) in the study region must be known a priori. As a field study, the cooperative inversion approach was used to identify the extension of uranium‐bearing target rock around the Beldih open cast mine. We noted the inconsistencies in both resistivity and density models obtained from the individual inversions. However, the presented cooperative inversion approach was able to produce similar resistivity and density models while maintaining the same error level of the respective individual inversions. We have considered four geologic units in the presented cooperative inversion as a field case study. We have also compared our cooperative results with drilled borehole and found to be a reliable tool to differentiate between the target rocks (kaolinite and quartz–magnetite–apatite rocks) and the ultramafic rock (host rock quartzite/alkaline granite). However, this study is subject to certain limitations such as the inability to differentiate between closely spaced kaolinite and quartz–magnetite–apatite rocks. |
| Author | Mishra, Pankaj K. Singh, Anand Sharma, S.P. |
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| Cites_doi | 10.1007/s10712-013-9232-4 10.1016/j.oregeorev.2014.12.015 10.1111/j.1365-246X.2010.04856.x 10.1017/CBO9781139167932 10.1088/0266-5611/13/1/006 10.1002/9781118929063 10.1190/geo2010-0255.1 10.1515/acgeo-2015-0071 10.1190/1.1443968 10.1029/2003JB002716 10.1111/1365-2478.12205 10.1007/s10712-017-9413-7 10.1007/978-1-4757-0450-1 10.1190/1.3513426 10.1190/geo2011-0154.1 10.1190/geo2014-0056.1 10.1111/j.1365-246X.2006.02923.x 10.1190/1.1444302 10.1190/geo2015-0147.1 10.1093/gji/ggw413 10.1111/j.1365-246X.2007.03366.x 10.1029/2012GL051233 10.1016/j.jappgeo.2017.11.014 10.1002/grl.50696 10.1190/geo2015-0457.1 10.1111/j.1365-2478.1996.tb00142.x 10.1190/1.1441501 10.1111/j.1365-246X.2010.04686.x 10.1111/1365-2478.12060 10.1190/geo2014-0122.1 10.4133/1.2923578 10.1007/s12594-014-0175-2 10.1190/geo2017-0040.1 10.1029/2003GL017370 10.1190/1.1442403 10.1190/segam2017-17790145.1 |
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Interpretation of a single geophysical data set is not sufficient to get complete subsurface information. Cooperative or joint inversion of... Interpretation of a single geophysical data set is not sufficient to get complete subsurface information. Cooperative or joint inversion of geophysical data... |
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| SubjectTerms | Algorithms Apatite Bearing Boreholes Case studies Clustering Cooperative inversion DC resistivity Density Direct current Electrical resistivity Geologic units Geology Geophysical data Geophysics Gravity Gravity data Inversions Kaolinite Magnetite Mathematical models Mineral exploration Petrophysical constraints Quartz Quartzite Rock Rocks Uranium |
| Title | 2D cooperative inversion of direct current resistivity and gravity data: A case study of uranium bearing target rock |
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